A flexible and effective linearization method for subspace learning

Nie, F; Xu, D; Tsang, IW; Zhang, C

A flexible and effective linearization method for subspace learning

Nie, F Xu, D Tsang, IW

Zhang, C

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Publication Type:: Chapter
Citation:: Graph Embedding for Pattern Analysis, 2013, pp. 177 - 203
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Nie, F	en_US
dc.contributor.author	Xu, D	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Zhang, C	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Graph Embedding for Pattern Analysis, 2013, pp. 177 - 203	en_US
dc.identifier.isbn	9781461444565	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119523
dc.description.abstract	© Springer Science+Business Media New York 2013. In the past decades, a large number of subspace learning or dimension reduction methods [2,16,20,32,34,37,44] have been proposed. Principal component analysis (PCA) [32] pursues the directions of maximum variance for optimal reconstruction. Linear discriminant analysis (LDA) [2], as a supervised algorithm, aims to maximize the inter-class scatter and at the same timeminimize the intra-class scatter. Due to utilization of label information, LDA is experimentally reported to outperform PCA for face recognition, when sufficient labeled face images are provided [2].	en_US
dc.relation.ispartof	Graph Embedding for Pattern Analysis	en_US
dc.relation.isbasedon	10.1007/978-1-4614-4457-2_8	en_US
dc.title	A flexible and effective linearization method for subspace learning	en_US
dc.type	Chapter
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© Springer Science+Business Media New York 2013. In the past decades, a large number of subspace learning or dimension reduction methods [2,16,20,32,34,37,44] have been proposed. Principal component analysis (PCA) [32] pursues the directions of maximum variance for optimal reconstruction. Linear discriminant analysis (LDA) [2], as a supervised algorithm, aims to maximize the inter-class scatter and at the same timeminimize the intra-class scatter. Due to utilization of label information, LDA is experimentally reported to outperform PCA for face recognition, when sufficient labeled face images are provided [2].

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http://hdl.handle.net/10453/119523